Keep in mind that you can create output arrays with more than 2 dimensions, but in the interest of simplicity, I will leave that to another tutorial.Įxamples: how to use the Numpy random normal function It will be filled with numbers drawn from a random normal distribution. normal will produce a Numpy array with 2 rows and 3 columns. You can also specify a more complex output.įor example, if you specify size = ( 2, 3 ), np. normal will provide x random normal values in a 1-dimensional Numpy array. The argument that you provide to the size parameter will dictate the size and shape of the output array. To learn more about Numpy array structure, I recommend that you read our tutorial on Numpy arrays. This might be confusing if you're not really familiar with Numpy arrays. Numpy arrays can be 1-dimensional, 2-dimensional, or multi-dimensional (i.e., 2 or more). Remember that the output will be a Numpy array. The size parameter controls the size and shape of the output. Hopefully, you're familiar with normally distributed data, but just as a refresher, here's what it looks like when we plot it in a histogram:īy default, the scale parameter is set to 1. The Numpy random normal function enables you to create a Numpy array that contains normally distributed data.Ī Quick Review of Normally Distributed Data In most cases, Numpy's tools enable you to do one of two things: create numerical data (structured as a Numpy array) or perform some calculation on a Numpy array. So Numpy is a package for working with numerical data in Python.Īs I mentioned previously, Numpy has a variety of tools for working with numerical data. Numpy random normal generates normally distributed numbers It also enables you to perform various computations and manipulations on Numpy arrays.Įssentially, Numpy is a toolkit for creating and working with arrays of numbers in Python. It enables you to collect numeric data into a data structure called the Numpy array. Specifically, Numpy performs data manipulation on numerical data. Numpy is a module for the Python programming language that's used for data science and scientific computing. The Numpy random normal function generates a sample of numbers drawn from the normal distribution, otherwise called the Gaussian distribution. And in particular, you'll often need to work with normally distributed numbers. If you're doing any sort of statistics or data science in Python, you'll often need to work with random numbers.
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